Method for monitoring component life

Abstract

Methods and systems are provided for reliably prognosing a vehicle component, such as a vehicle battery or an intake air filter. A state of degradation of the component is predicted based on a metric that is derived from a sensed vehicle operating parameter, the parameter selected based on the component being diagnosed, as well as based on past driving history and future driving predictions. The predicted state of degradation is then converted into an estimate of time or distance remaining before the component needs to serviced, and displayed to the vehicle operator.

Description

FIELD[0001]The present application relates to methods performed in vehicles, such as hybrid vehicles, for estimating the remaining life of a vehicle component using statistical predictions.BACKGROUND AND SUMMARY[0002]Vehicles include various components which degrade at different rates and have to be serviced at different times. In addition, the degradation rate of each component may be affected by multiple parameters, some of which are overlapping with other components while others are non-overlapping. For example, in hybrid electric vehicles, a system battery may degrade based on the rate of battery usage, the age of the battery, temperature conditions, the nature of the battery, etc. As another example, an air filter coupled to the engine intake may degrade based on the age of the filter, air quality, ambient weather conditions, etc.[0003]Various approaches have been developed to predict the state of health of a vehicle component. One example approach is shown by Uchida in U.S. Pa...

AI Technical Summary

Benefits of technology

[0007]In this way, the remaining life of a vehicle component may be accurately predicted without relying on computationally intensive algorithms. By using data sensed on-board the vehicle, in association with vehicle driving statistics, the state of health of a component may be calculated more accurately. For example, the internal resistance and capacitance of a system battery may be better determined by accounting for temperature effects, as well as the effects of aggressive operator driving behavior. As another example, the degree of clogging of an air filter may be more accurately predicted based on a recursive estimation of mean and standard deviation of air flow values at large throttle openings. By assessing an air filter while relying on air flow or manifold pressure data sensed during vehicle transients, a larger portion of data collected over a vehicle drive cycle can be leveraged for filter prognostics. In addition, the need for actively holding the engine in a defined speed-load region, to complete a prognostic or diagnostic routine, is reduced. By converting the sensed state of health into an estimate of a remaining time or duration of vehicle operation before component servicing is required, a vehicle operator may be better notified

Problems solved by technology

As another example, an air filter coupled to the engine intake may degrade based on the age of the filter, air quality, ambient weather conditions, etc.

However the inventors herein have identified various issues with such approaches.

As one example, the above approaches rely on statistical analyses that can be computationally intensive.

Consequently, they may require extensive memory and processor resources to assess the health of the battery.

Reliance on sensors, which themselves are subject to wear and tear, can cause inaccuracy in the state of health estimation.

In addition, the approach does not accurately account for the effect of temperature on the internal resistance

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